Unsupervised asymmetry detection

ABSTRACT

Asymmetries are detected in one or more images by partitioning each image to create a set of patches. Salient patches are identified, and an independent displacement for each patch is identified. The techniques used to identify the salient patches and the displacement for each patch are combined in a function to generate a score for each patch. The scores can be used to identify possible asymmetries.

BACKGROUND

The present disclosure relates to image analysis, and more specifically,to asymmetry detection.

Asymmetry detection is used, for example, in the medical field toidentify tumors and other abnormalities in organs. Asymmetry detectioncan use a variety of methods to automate image analysis. The variousmethods are associated with a range of accuracies and costs.

SUMMARY

Embodiments are directed toward a method for comparing images. Themethod can include identifying a first image and a second image, andpartitioning each image to create a set of patches for each image whereeach patch contains a plurality of pixels. The method can furtherinclude determining a difference between at least a portion of thepatches of each image and an average patch of the image, and identifyinga minimum cost translation for each patch of the first image to acorresponding patch of the second image where each translation isindependent, and where the minimum cost translation comprises a minimumchange in a set of patch statistics between a first patch, or portionthereof, of the first image and the corresponding patch, or portionthereof, of the second image. The method can further include calculatinga score for at least a portion of the patches where each score includesthe difference and the minimum cost translation.

Various embodiments are directed toward a system for comparing images.The system can include a memory configured to store a first image and asecond image, a graphical display configured to display an output, and aprocessor coupled to the graphical display and to the memory. Theprocessor can be configured to obtain the first image and the secondimage from the memory, and to partition each of the first and secondimages to create a plurality of patches for each image where each patchcomprises a plurality of pixels. The processor can be further configuredto determine a difference between at least one patch, or portionthereof, of each image and an average patch, or portion thereof, of theimage, and to identify a selective shift for each patch, or portionthereof, of the first image to a corresponding patch, or portionthereof, of the second image, where each shift is independent of eachother shift, and where the selective shift represents a minimum changein a set of pixel statistics between a respective patch, or portionthereof, of the first image and the corresponding patch, or portionthereof, of the second image. The processor can be further configured tocalculate a score for at least a portion of the patches where the scoreincludes the difference and the selective shift. The processor can befurther configured to output at least one score to the graphical displayfor displaying on the graphical display.

Certain embodiments are directed toward a computer program product foridentifying asymmetries between a plurality of images. The computerprogram product can include a computer readable storage mediumcontaining program instructions which are executed by a processor. Theinstructions executed by the processor can cause the processor toidentify a first image and a second image where the first image and thesecond image include two discrete images or two portions of a same imagewhere the two portions are separated according to an approximate axis ofsymmetry. The instructions executed by the processor can further causethe processor to partition each image to create a set of patches whereeach patch comprises a plurality of pixels, and to identify salientpatches of each image. The instructions executed by the processor canfurther cause the processor to identify a respective patch displacementfor at least the salient patches such that each displacement isindependent. The instructions executed by the processor can furthercause the processor to present a score for at least each salient patchwhere the score comprises the saliency and the patch displacement for atleast each salient patch.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 illustrates a flowchart of one example embodiment for identifyingasymmetries between two images.

FIG. 2 illustrates a flowchart for identifying asymmetries between twoimages according to various embodiments of the present disclosure.

FIG. 3 is a block diagram of a host device according to some embodimentsof the present disclosure.

FIG. 4A presents an experimental saliency graph of two mammograms fromthe same patient according to some embodiments of the presentdisclosure.

FIG. 4B presents an experimental saliency graph of two mammograms fromdifferent patients according to some embodiments of the presentdisclosure.

FIG. 5A shows experimental changes in Dice score as a function of thepercentage of dislocated patches for left and right translations andclockwise and counterclockwise rotations in accordance with someembodiments of the present disclosure.

FIG. 5B shows experimental correlations between patches as a function ofa percentage of dislocated patches for left and right translations andclockwise and counterclockwise rotations according to some embodimentsof the present disclosure.

While the present disclosure is amenable to various modifications andalternative forms, specifics thereof have been shown by way of examplein the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the presentdisclosure to the particular embodiments described. On the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present disclosure.

DETAILED DESCRIPTION

Computational image analysis can automate and standardize theinterpretation of information contained in various images. Images cancontain significant amounts of data which can result in impracticalanalysis using some computational methods. Furthermore, individual imageanalysis by trained professionals is costly. Thus, an expeditious,consistent, and accurate method of computational image analysis mayprovide numerous advantages to, for example, the medical industry.

A specific field of image analysis involves medical image analysis.Medical image analysis characterizes the information contained in imagesgenerated by methods such as, but not limited to, electron microscopy(EM), x-ray computed tomography (CT), magnetic resonance (MR), singlephoto emission computed tomography (SPECT), positron emission tomography(PET), and computed radiography (CR), among others. Medicalprofessionals can use these images to identify health issues. One suchhealth issue is tumor identification.

Asymmetry detection is one strategy employed by some medicalprofessionals to identify tumors. Asymmetry detection compares images ofa pair of organs to identify discrepancies between the two images whichmay constitute a tumor. Asymmetry detection can be similarly employed incases where the same body part is imaged at two different times.Asymmetry detection can be further similarly employed in cases where asingle, symmetric organ (e.g., the brain) is compared for internalasymmetries with respect to an internal axis of symmetry (e.g., theMid-Sagittal Plane of a brain).

Asymmetry detection techniques can be categorized as supervised orunsupervised. Supervised asymmetry detection includes “training” in theform of a set of reference images demonstrating true and false results(i.e., images containing identified examples of asymmetries). Thus, atechnique employing supervised asymmetry detection compares a queryimage to a set of reference images with known true and false readings,and the technique determines a true or false reading based on the levelsof similarity between the query image and the various reference images.Unsupervised asymmetry detection relies on the information providedwithin the given image data to determine if asymmetries are present.Thus, a technique employing unsupervised asymmetry detection mayidentify asymmetries between a set of images, or within a single,approximately symmetric image, using only the information containedwithin the image(s).

Asymmetry detection techniques can be further characterized as requiringor not requiring alignment. Asymmetry detection techniques requiringalignment necessitate approximately identical alignment to produceapproximately identical image positioning. Following consistentalignment, the image analysis system compares each portion of one imagewith the corresponding (according to position) portion of the secondimage. Accurate image alignment is difficult to achieve due to thenumber of variables which affect each image. For example, some variableswhich may affect consistency between images are patient shifting,patient breathing, patient weight change, patient bladder fullness,variations between techniques of medical professionals, and so on.

Advantageously, the present disclosure provides a method and system forasymmetry detection which is unsupervised and does not requirealignment. Unsupervised asymmetry detection can reduce costs associatedwith “teaching” a program to identify asymmetries. Furthermore,asymmetry detection which does not require alignment can reduce thenumber of false asymmetry detections. It should be understood that notall advantages of the present disclosure are herein described.Furthermore, embodiments of the present disclosure exist which containnone, some, or all of the presently listed advantages while remainingwithin the spirit and scope of the present disclosure.

In some embodiments, the present disclosure identifies asymmetries byidentifying sets of pixels which are distinct both internally within animage and externally between a pair of images. The distinctness (i.e.,saliency) of a set of pixels within an image is characterized by adegree of difference of the patch under consideration compared to theaverage patch of the image. A set of pixels is externally salient when abest-match pair of patches exhibit a high degree of difference comparedto other best-match pairs of patches.

Referring now to the figures, FIG. 1 illustrates a flowchart of oneexample embodiment for identifying asymmetries between an approximatelysymmetric image or between two images. The method 100 starts byidentifying images in operation 110. The images can be any type of imageincluding, but not limited to, electron microscopy (EM), x-ray computedtomography (CT), magnetic resonance (MR), single photo emission computedtomography (SPECT), positron emission tomography (PET), and computedradiography (CR), among others. The images can be two dimensional orthree dimensional, or a set of two dimensional images wherein theplurality of images represents a third dimension.

The images can be comprised of data points, such as, for example,pixels. The data points can contain numerous types of information whichcan be collectively referred to as statistics (e.g., pixel statistics,patch statistics, etc.). For the purposes of the present disclosure, thestatistics of an image, or portion thereof, refer to any data containedwithin an image which reflects the color, brightness, darkness,contrast, hue, luminance, or other visual characteristic of the image,or portion thereof. In some embodiments of the present disclosure, theimage statistics may be represented as a binary number (e.g., foregroundor background), a single number (e.g., grayscale images wherein eachnumber represents the brightness of the pixel from black—no brightness,to white—maximum brightness), or a set of numbers (e.g., the RGB colorspace wherein a pixel comprises three vectors, or, alternatively,wherein a pixel comprises three “grayscale” numbers, each one associatedwith red, green, and blue respectively). It should be understood thatthe cited illustrative image statistics are described by way of exampleonly and should not be construed as limiting for the purposes of thepresent disclosure.

In operation 112 (indicated as optional via the dotted box), a line ofsymmetry can be identified in each image. For example, if a single imageis selected in operation 110, a line of symmetry can be created inoperation 112 to effectively partition the single image into two imagesfor comparison. In an alternative example, if a plurality of images areselected in operation 110, a line of symmetry can be created in eachimage. In such an example, multiple images each containing at least oneline of symmetry can be used to monitor asymmetry (e.g., tumor) growthor reduction over time. That is to say, in an illustrative first brainscan, a line of symmetry can be created and asymmetries identifiedbetween the approximately symmetric halves. An illustrative second brainscan taken at a later time can be similarly compared across an internalline of symmetry for asymmetries. Subsequently, the asymmetriesidentified in the first brain scan can be compared to the asymmetriesidentified in the second brain scan to identify changes in the one ormore asymmetries over time.

Although operation 112 is shown occurring between operations 110 and120, in alternative embodiments operation 112 occurs as a sub-componentof operation 140 where salient patches are identified. Thus, methodsemployed in later operations can also determine the probable line ofsymmetry within a given image.

In operation 120, the images are partitioned. The partitioning resultsin a plurality of patches. Each patch comprises a plurality of datapoints (e.g., pixels). The patches can be two-dimensional or threedimensional, and can comprise any geometry such as, but not limited to,square, rectangular, triangular, cubic, conical, and so on. Partitioningand patches as they relate to the present disclosure are described infurther detail herein with respect to operation 220 of FIG. 2.

In operation 140 salient patches are identified. In some embodiments ofthe present disclosure, the salient patches are identified as patches ofan image which are both internally and externally salient. Patches areinternally salient in cases where the patch statistics are not readilydescribable by surrounding patches. In some embodiments, the patchstatistics of one or more patches are compared to the statistics of theaverage patch of the image. In some embodiments, this comparison is madeusing principal component analysis (PCA). Thus, internally salientpatches exhibit a high degree of difference (compared to other patcheswithin the image) with respect to the average patch of the imageaccording to the direction of, and/or the variation along, one or moreprincipal components of the patch under consideration and the averagepatch of the image. In some embodiments, the analysis of internalsaliency is conducted for patches which exhibit an external saliencyabove a first threshold.

In some embodiments, patches are externally salient in cases where thedifference between the respective patch statistics of the first imageand the respective patch statistics of the closest matched patch of thesecond image is above a first threshold. In some embodiments, externalsaliency can be defined by the difference in variation exhibited alongthe same principal components of the two patches. In various embodimentsthe first threshold is user defined or an automatically selected numberor percentage.

In operation 150 patch displacement between the two images ischaracterized. Operation 150 can account for differences in alignment(i.e., orientation) and/or size (i.e., amount of zoom) between theimages under consideration. In some embodiments, patch displacement ischaracterized by independently applying randomized displacement vectorsto patches, or portions thereof, and calculating a similarity valuebetween the patch statistics of the patch, or portion thereof, of thefirst image with the patch statistics of the displaced patch, or portionthereof, of the second image. In some cases, the process is iterative,and thus, numerous calculations can be conducted wherein each iterationis localized to the area of the best match of the previous iteration. Insome alternative embodiments, each iteration can be localized to a rangeof directions or magnitudes based on the previous iteration(s). Thus,subsequent iterations can use a set of randomized displacement vectorshaving a limited range of randomness where the limited range ofrandomness is a function of the previous iteration(s) and limits thedirection, the magnitude, or both the magnitude and the direction of thesubsequent set of random displacement vectors.

In operation 160 the results of the analysis are consolidated andpresented. In some embodiments, the results of operations 140 and 150are presented separately. In some embodiments the results of operations140 and 150 are presented by a single, integrated score (i.e., asaliency score). In some embodiments the saliency score is a function ofthe salient patch measures and the patch displacement measures as willbe further described herein with respect to FIG. 2.

The results can be presented in a variety of ways. For example, theresults can be presented via a graph, a table, or an image. In caseswhere the results are presented as an image (e.g., an overlay on theoriginal image(s) identified in operation 110), the results can indicatethe location of the identified asymmetry or asymmetries within theimage.

Referring now to FIG. 2, illustrated is a flowchart for identifyingasymmetries between a single, approximately symmetric image or betweentwo images according to various embodiments of the present disclosure.The method 200 can begin with identifying the query image or images inoperation 210. In various embodiments, either a single image or aplurality of images are identified. In cases where a single image isidentified, the method 200 determines at least one line of symmetrywithin the image as indicated by optional operation 212. Thus, in caseswhere a single image is identified, the method 200 seeks asymmetriesbetween at least two symmetrical portions of the single image. In caseswhere a plurality of images are identified, the method 200 seeksasymmetries between each respective image. For the purposes of thepresent disclosure, in cases where two images are cited, it should beunderstood that the two images can refer to two discrete images or totwo portions of an approximately symmetric image where the two portionsare separated by the line of symmetry identified in operation 212.Operation 212 can occur between operations 210 and 220 as shown, or,alternatively, operation 212 can occur as a sub-operation of, forexample, operation 230 wherein the method employed by operation 230 can,in some embodiments, automatically identify a line of symmetry within agiven image.

In operation 220, the patches are identified for each respective image,or, in the case of a single image, for each symmetric portion of theimage. An image patch is a sub-area of an image. In some embodiments, animage patch is measured by pixels. In alternative embodiments, an imagecan be separated into a given number of sections. For example, an imagecan be broken into 256 sections comprising a 16×16 square of patches.Alternatively, an image can be segmented into 5×5 pixel square patcheswith the number of patches being a function of the total number ofpixels of the image. Although the previous examples use square patches,rectangular, polygonal, ovoidal, or other regular or irregular geometricshapes can be similarly used to define the various patches. Furthermore,patches in excess of two dimensions are possible, such as, but notlimited to, cubic patches, in cases where the image(s) are threedimensional.

Operation 240 computes cross patch distinctness (CPD) scores. In someembodiments, the CPD score is calculated for each respective pair or setof patches. In alternative embodiments, the CPD score is calculated fora subset of the total number of patches. The CPD calculation accountsfor patch distinctness (i.e., saliency) within and between images. TheCPD calculation can use a gating variable, namely, c_(j), such that thegating variable moderates the influence of each principal component (PC)based on cross-correlations between the two images under comparison. Insome embodiments, the equation for CPD is:CPD(p _(x,y))=Σ_(j=1) c _(j) |p _(x,y) w _(k) ^(T) |c _(j)ϵC,Cϵ{0,1}^(n)

In the previous equation, p_(x,y) is a vectorized patch and w_(k) ^(T)is the k'th principal component of the distribution of all patches inthe image. Thus, a larger score indicates increasing distinctness of agiven patch compared to the other patches of the image. The CPD scoreintroduces the gating variable c_(j) which excludes consideration ofsome patches which are not sufficiently distinct to be considered apossible asymmetry. Gating variable c_(j) reduces the number ofcomputations, and, thus, reduces the amount of resources and timerequired to conduct asymmetry analysis according to some embodiments ofthe present disclosure. Gating variable c_(j) can be considered a firstthreshold in some embodiments of the present disclosure. The gatingvariable c_(j) can be user defined or automatically selected, and it canconstitute a number or a percentage of the results.

In some embodiments, respective patches of each image are matched usinga bi-partite matching method. As is understood by one skilled in theart, bi-partite matching finds the minimum cost set of associationsuniquely connecting one group of data to a second group of data. That isto say, each data point of the first set is connected to one and onlyone data point of the second set. In embodiments of the presentdisclosure, the first set of data are the PCs of the patches of thefirst image and the second set of data are the PCs of the patches of thesecond image. In embodiments, the PCs of each image are matched usingthe cosine distance as the cost. The cosine distance is defined as:cost=1−cos θ

In the above equation, θ represents the angle formed between the PCs ofthe matched patches. Thus, cost will be minimized in cases where θ isminimized. The angle θ will be minimized in cases where the principalcomponents of the matched pair approach parallel.

Following the matching, gating vectors C_(1,2) are defined according tosome embodiments. In some embodiments, the gating vectors are used toselect a subspace of the patches which will be used in the finalanalysis and/or the CPD equation. The subspace, as opposed to the fullspace, expedites the process by reducing the number of computationaloperations required. Thus, for the first and second images respectively,the PCs or patches are selected according to gating variables determinedby the following equations:C ₁=arg max Σ_(j) c _(j)(ν_(j) ¹−ν_(j) ²)−λ∥C ₁∥₁ c _(j)ϵ{0,1}C ₂=arg max Σ_(j) c _(j)(ν_(j) ²−ν_(j) ¹)−λ∥C ₂∥₁ c _(j)ϵ{0,1}

In the above equation, ν_(j) ², ν_(j) ¹ represents the relative varianceof the j'th PC of the second and first images respectively. The λ∥C₁∥₁term is a normalizing term used to simplify the output of thecomputation. Thus, patches are considered externally salient wherein thematched pair of patches exhibits a large degree of difference invariation along similar PCs. Specifically, in some embodiments,externally salient patches are defined in cases where the difference invariation between the matched patches is above a first threshold. Invarious embodiments, the first threshold refers to c_(j), C₁, C₂, or anycombination of the aforementioned.

In addition to calculating the CPD distinctness score, aspects of thepresent disclosure calculate image patch flow between two images toaccount for translations, rotations, or other variations in alignmentbetween the two images. Thus, in operation 250, a cross image patch flowis calculated for each pair or set of patches, or for a subset of thetotal number of patches according to various embodiments. The imagepatch flow method measures how well each patch fits the smooth motionfield assumption. The smooth motion field assumption allows each patchto move independently of each other patch (e.g., considering patchdisplacements measured in a polar coordinate system, one hypotheticalpatch can be translated 5 units at 35 degrees while a different patchcan be translated 1 unit at 280 degrees). Thus, embodiments of thepresent disclosure using the patch flow methods as disclosed herein,allow for two images to be implicitly aligned (i.e., using the patchflow method herein disclosed) as opposed to requiring an alignment step.To test the smooth motion field assumption, a randomized correspondencealgorithm (e.g., PatchMatch) can be used for each patch as follows:

D(p_(x, y), p_(x + T_(x), y + T_(y))) = Σ_(i = x − n/2)^(i = x + n/2)Σ_(j = y − n/2)^(j = y + n/2)I₁(i, j) − I₂(i + T_(x), j + T_(y)₂

The randomized correspondence algorithm applies at least one randomdisplacement vector T to at least one pixel location (x,y) of eachpatch. The displacement vector T marks the hypothetical, shiftedlocation of the same pixel location in the second image. I_(1,2) canrepresent image intensity values of the respective pixels (or otherstatistics), and thus, provides a measure of similarity between thepixel of the first image and the pixel located at the predetermineddisplacement of the second image. The best match displacement vector canbe selected from each set of randomized displacement vectors. Theprocess can be iterated multiple times using propagated and randomdisplacements to converge to a sufficient approximation of the flowfield under the smooth motion field assumption. In some embodiments,four to five iterations result in convergence to an approximation of theflow field under the smooth motion field assumption. Thus, following asufficient number of iterations, the two images can be appropriatelymatched regardless of original discrepancies in orientation, position,and/or subject size (i.e., zoom level) of the respective images.

Following the randomized correspondence algorithm, operation 260measures the consistency between each patch and the smooth motion fieldassumption by identifying nearest neighbor error (NNE) according to thefollowing equation.NNE(p _(x,y))=min_(T) D(p _(x,y) p _(x+T) _(x) _(,y+T) _(y) )

The nearest neighbor error is minimized for all pixels, all patches, asubset of pixels, a subset of patches, or a subset of pixels in a subsetof patches. Thus, the nearest neighbor error provides a second score,similar to the CPD score, which indicates the degree of similaritybetween a portion of the first image and its most similar counterpart inthe second image.

In operation 270 the final cross saliency score (CS) is calculated. Invarious embodiments, the final score uses both CPD and NNE measurements.Both CPD and NNE measurements are normalized to allow for appropriateintegration of the two measures. The CS can be described by thefollowing equation:

${{CS}\left( {x,y} \right)} = {{\frac{1}{2l}\Sigma_{s = 1}^{l}{{CPD}\left( p_{x,y} \right)}} + {{NNE}\left( p_{x,y} \right)}}$

In some embodiments, l represents the number of pixels of each patchwhich were measured by the CPD and NNE methods.

Lastly, in operation 280, the cross saliency scores are presented. Thepresentation can be in the form of a graph, a table, and/or the originalimage(s) overlaid with the cross-saliency scores for each respectivepatch of the one or more images. In some embodiments, the cross saliencyscores are presented to a user via a graphical display.

It should be understood that although the method 200 comprisingoperations 210 through 280 is presented in a serial fashion, not alloperations must necessarily occur in the order presented. For example,calculation of the CPD values can occur in parallel with the calculationof the cross-image patch flow and NNE values. Similarly, operation 280can occur multiple times throughout the method 200, or can present theindividual results of multiple operations of the method 200. Furtherstill, operations can be added to the method 200, or existing operationsillustrated with respect to the method 200 can be removed whileremaining within the spirit and scope of the present disclosure.

With reference now to FIG. 3, shown is a block diagram of a host deviceaccording to some embodiments of the present disclosure. The host device300 can include, without limitation, one or more processors (CPUs) 305,a network interface 315, an interconnect 320, a memory 325, and astorage 330. The host device 300 can also include an I/O deviceinterface 310 connecting I/O devices 312 (e.g., keyboard, display, andmouse devices) to the host device 300. The I/O devices 312 are capableof receiving a user-defined input and are capable of outputtingasymmetry detection results.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 can be implemented using one or more busses. TheCPUs 305 can be a single CPU, multiple CPUs, or a single CPU havingmultiple processing cores in various embodiments. In one embodiment, aprocessor 305 can be a digital signal processor (DSP). Memory 325 isgenerally included to be representative of a random access memory (e.g.,static random access memory (SRAM), dynamic random access memory (DRAM),or Flash). The storage 330 is generally included to be representative ofa non-volatile memory, such as a hard disk drive, solid state device(SSD), removable memory cards, optical storage, or Flash memory devices.In an alternative embodiment, the storage 330 can be replaced by storagearea-network (SAN) devices, the cloud, or other devices connected to thehost device 300 via the communication network 380 or the I/O devices312.

The network 380 can be implemented by any number of any suitablecommunications media (e.g., wide area network (WAN), local area network(LAN), Internet, Intranet). The network 380 can be implemented within acloud computing environment, or using one or more cloud computingservices. Consistent with various embodiments, a cloud computingenvironment can include a network-based, distributed data processingsystem that provides one or more cloud computing services. Further, acloud computing environment can include any number of computers disposedwithin one or more data centers and configured to share resources overthe network 380.

The memory 325 stores asymmetry detection instructions 328. The storage330 stores one or more images 334. Alternatively, the images 334 and theinstructions 328 can be stored partially in memory 325 and partially instorage 330, or they can be stored entirely in memory 325 or entirely instorage 330, or they can be accessed over the network 380.

The asymmetry detection instructions 328 contain patch distinctnessinstructions 336 and patch flow instructions 338. In some embodiments,the asymmetry detection instructions 328 are executed by one or moreCPUs 305 to analyze one or more images 334. The asymmetry detectioninstructions 328 can provide instructions matching, in whole or in part,the methods as described with reference to FIG. 1 and FIG. 2. The patchdistinctness instructions 336 can provide instructions executable by aprocessor 305 similar to, in whole or in part, operations 140 of FIG. 1and operation 240 of FIG. 2. The patch flow instructions 338 can provideinstructions executable by a processor 305 similar to operations 150 ofFIGS. 1 and 250 and 260 of FIG. 2.

As described herein, the present disclosure has numerous applications tonumerous fields. Thus, it may be helpful to describe experimentalresults of exemplary embodiments of the present disclosure to furtherenable a person of skill in the art to appreciate the practicalapplication of some embodiments of the present disclosure. It should beunderstood that the following experimental results are disclosed hereinonly for exemplary purposes and should not be construed as limiting tothe various embodiments of the present disclosure. Embodiments of thepresent disclosure can display similar or dissimilar experimentalresults as disclosed with respect to the following experiments whileremaining within the spirit and scope of the present disclosure.

Referring now to FIG. 4, shown is an example set of results inaccordance with some embodiments of the present disclosure. FIG. 4Apresents a saliency graph of two mammograms from the same patient whileFIG. 4B presents a saliency graph of two mammograms from differentpatients. As shown, the y-axis 410A and 410B presents the 16 selectedprincipal components of the second image (im2) which are ordered frommost variation (top) to least variation (bottom). Similarly, the x-axis420A and 420B presents the 16 selected principal components of the firstimage (im1) from most variation (left) to least variation (right). Thevalues of the cells created by the graph are notated by the scale 430Aand 430B. In some embodiments, the scale is the cosine of the angleformed between the principal component of the patch corresponding to thelocation on the y-axis 410A and 410B and the principal component of thepatch corresponding to the location on the x-axis 420A and 420B for eachrespective combination of principal components presented by the grid. Insome embodiments the values on the scale are presented in color while inalternative embodiments the values on the scale are presented ingrayscale.

As can be seen in FIG. 4A, an approximately diagonal line is formed fromthe top left to the bottom right of the grid. The diagonal line iscomprised of values approximately equal to 1 or −1 as indicated by thescale 430A. The diagonal line represents matched variation rankingsalong the principal components of the respective patches of the twoimages. That is to say, the patch of image 1 having the highest degreeof variation along its principal component and the patch of image 2having the highest degree of variation along its principal component areapproximately parallel as indicated by the value of approximately 1 or−1 wherein the value represents the cosine of the angle formed betweenthe two principal components of the two image patches. Thus, theprincipal components of the patches of each image having the samerelative degree of variation (with respect to the other PCs of therespective images) will be approximately parallel to one another inimages of approximately the same subject.

Asymmetries can be detected in cases where the diagonal line ofapproximately 1 or −1 values is discontinuous. As can be seen in FIG.4A, the third and fourth principal components of image 1 and image 2disrupt the diagonal pattern. This indicates a possible asymmetrybetween the images at the patches of the principal components having thethird and fourth highest levels of variation along their respective PCs.

In contrast, as can be seen in FIG. 4B presenting the saliency graph oftwo mammograms from two different patients, the diagonal aligning PCswith similar relative degrees of variation is not well defined. Thisdemonstrates that the PCs having similar relative degrees of variationare not approximately parallel to one another, and, thus, are not verysimilar. Furthermore, the comparison of FIG. 4A to FIG. 4B demonstratesthat, regardless of orientations or translations of the two images withrespect to one another, the principal component of the patch having thehighest degree of variation in the first image and the principalcomponent of the patch having the highest degree of variation in thesecond image reflects approximately the same portion of each imageunless there is an asymmetry.

Thus, FIG. 4 demonstrates the utility of some embodiments of the presentdisclosure. Specifically, FIG. 4A in comparison to FIG. 4B demonstratesthe principal components of each patch of each image, ordered accordingto the degree of variation, have similar orientations in cases where theimages are approximately similar. However, discontinuities, as seen inthe third and fourth principal components of FIG. 4A occur in patcheswhich can represent asymmetries (e.g., tumors) in the various images.

In addition to the experimental output illustrated in FIG. 4demonstrating the integrity of image analysis using some embodiments ofthe present disclosure, two experiments are further conducted to measurethe validity of some embodiments of the present disclosure in comparisonto other methods.

A first experiment involved tumor detection of 20 skull-strippedmulti-modal magnetic resonance (MR) images including high grade gliomas(BRATS2014 training set). Thus, the first experiment seeks asymmetriesbetween a symmetric single image (i.e., the left and right sides of thebrain). In the first experiment, patches are created having dimensions3×3×4 cubes in two scale wherein the third dimension represents the fourMRI channels. A cross saliency map is created for each slice, and asalient volume is identified by identifying a set of salient regionswithin the various cross saliency maps. In some embodiments,identification of the salient volume uses mean-shift clusteringtechniques to identify salient regions. In some embodiments, the salientvolume comprises the chain of salient regions having the maximumsaliency score at their respective centroids.

The results of the tumor detection are compared to the ground truth(e.g., the accepted size, shape, and location of the gliomas) using theDice score. The average Dice score is 0.753±0.039. For reference, apublished experiment using the random forest (RF) method generated anaverage Dice score of 0.763±0.027. Thus, as appreciated by one skilledin the art, the method employed in some embodiments of the presentdisclosure achieves similar results as other methods while providing theadded benefits of, though not limited to, being an unsupervised methodand not requiring image alignment in various embodiments.

Referring now to FIG. 5, shown are graphs monitoring the integrity ofthe method used in the first experiment as a function of dislocatedpatches resulting from variations in the line of symmetry. The resultsof the first experiment were checked for stability under variation inMid-Sagittal Plane Estimation. Specifically, the stability testevaluated the robustness of the data in events where the line ofsymmetry may be incorrectly calculated (or, in this case, intentionallyaltered). The mid-sagittal line was translated 1 to 15 pixels in eitherdirection, or rotated 1 to 15 degrees both clockwise andcounterclockwise. Each shift dislocates approximately 2.5% of thepatches from one side of the image to the other side of the image.

FIG. 5A shows the changes in Dice score 510A as a function of thepercentage of dislocated patches 520A for left and right translationsand clockwise and counterclockwise rotations. As can be seen, themajority of the Dice scores remain within the error of the average Dicescore (0.753±0.039). Thus, an additional advantage of some embodimentsof the present disclosure is robustness of the analysis in cases wherethe line of symmetry may be mis-identified.

FIG. 5B shows the level of correlation 510B of the method of the firstexperiment between patches as a function of percentage dislocatedpatches 520B for left and right translations and clockwise andcounterclockwise rotations. As shown, the correlation is greater than orequal to 0.95 for patch dislocation up to 7.5% and greater than or equalto 0.90 for patch dislocation up to 22.5% in various shifts. Thus, FIG.5B demonstrates robustness of cross correlations in cases where the lineof symmetry may be mis-identified.

A second experiment involved tumor detection in breast mammograms. Thus,the second experiment seeks asymmetries between a symmetric pair oforgans (i.e., left and right breasts). The second experiment evaluatedleft and right mediolateral oblique view (MLO) scans of 40 patientsreceiving full-field digital (FFD) mammograms. Each tumor was delineatedby a radiologist. A total of 42 tumors ranging in size from 4 to 83 mmwere identified.

The second experiment evaluated the mammogram data using threetechniques. The first technique employed spatial techniques (i.e., patchintensity histogram, mean intensity, standard deviation of intensities,and relative position to the nipple in polar coordinates). The secondtechnique aligned the images using thin-plate spline (TPS) alignment,followed by the same spatial features previously mentioned in additionto an earth movers distance (EMD) flow vector. The third technique (thetechnique herein disclosed according to some embodiments) uses spatialfeatures, CPD, and NNE measures computed on 9×9 patches in fourdown-sampling factors (2, 4, 8, 16). The results of the hereinabovedescribed test are recorded in Table 1:

TABLE 1 Spatial Spatial Features + Spatial Features + Measure FeaturesTPS + EMD CPD + NNE AUC 0.88 0.88 0.93 Sensitivity 0.72 ± 0.05  0.82 ±0.002 0.88 ± 0.03  Specificity 0.82 ± 0.013 0.73 ± 0.008 0.82 ± 0.018

Area under the curve (AUC), sensitivity, and specificity were measuredfor comparison purposes of the three techniques previously described.AUC refers to the overall accuracy of the method (i.e., AUC accounts forpositively identified positives, negatively identified positives,negatively identified negatives, and negatively identified positives).Sensitivity refers to the ability of the technique to correctly identifyparts of the tumor. Specificity refers to the ability of the techniqueto correctly identify parts which do not comprise a tumor.

Thus, as can be seen, embodiments of the method described in the presentdisclosure produced a higher AUC score compared to both competitors, ahigher sensitivity score compared to both competitors, and an equal arehigher specificity score compared to both competitors. Thus, the resultsof the second experiment demonstrate the increased accuracy of someembodiments of the present disclosure over alternative methods.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed approximately concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for comparing images, the methodcomprising: identifying a first image and a second image; partitioningeach image to create a respective set of patches for each image, whereineach patch comprises a plurality of pixels; determining a respectivedifference between at least a portion of the patches of each respectiveimage and an average patch of the respective image; identifying arespective minimum cost translation for each patch of the first image toa corresponding patch of the second image, wherein each translation isindependent of each other translation, wherein the minimum costtranslation comprises a minimum change in a set of patch statisticsbetween a first patch, or portion thereof, of the first image and thecorresponding patch, or portion thereof, of the second image; andcalculating a respective score for at least a portion of the patches,wherein each score comprises the respective difference and therespective minimum cost translation.
 2. The method of claim 1, whereindetermining a respective difference further comprises: determining atleast one respective principal component of each image; determining atleast one respective principal component of each patch of each image;matching each patch of the first image to a nearest match patch of thesecond image to create a set of matched patches; selecting, based on thematching, a subset of patches of the first image and a subset of patchesof the second image; and determining a respective difference betweeneach patch of the subset of patches and the average patch for eachrespective image, the difference comprising a difference in patchstatistics, the difference being measured with respect to at least oneprincipal component of the respective image.
 3. The method of claim 2,wherein selecting, based on the matching, a subset of patches of thefirst image and a subset of patches of the second image comprisesselecting the subset of patches having a difference in variance above afirst threshold, wherein the difference in variance is based on avariance with respect to a principle component of a first patch of thefirst image and a variance with respect to a same principle component ofa second patch of the second image, wherein the first patch and thesecond patch are a pair of patches in the set of matched patches.
 4. Themethod of claim 2, the matching further comprising: calculating the atleast one respective principal component of each patch of each image;identifying a plurality of sets of associations, wherein each respectiveset of associations comprises a plurality of associations such that eachpatch of the first image is uniquely matched to a respective patch ofthe second image; calculating a respective cost of each set ofassociations, wherein the cost comprises a sum of each respective costof each association; and selecting the set of associations having alowest cost.
 5. The method of claim 4, wherein calculating a respectivecost of each set of associations comprises summing, for each pair ofpatches in the set of associations, a respective cost based on an angleformed between a principal component of a first patch and a principalcomponent of a second patch, wherein the first patch and the secondpatch are a pair of patches in the respective set of associations. 6.The method of claim 1, wherein identifying a minimum cost translationfurther comprises: assigning a first set of random displacement vectorsto at least one pixel of a selected patch of the first image, whereinthe first set of random displacement vectors has a first range ofrandomness, computing, for each random displacement vector in the firstset of random displacement vectors, a respective difference in pixelstatistics of each at least one pixel of the selected patch of the firstimage to a corresponding at least one pixel of a second patch of thesecond image, wherein the corresponding at least one pixel of the secondpatch is determined by the random displacement vector of the first setof random displacement vectors; selecting a first displacement vector ofthe first set of random displacement vectors having a lowest differencein pixel statistics; assigning a second set of random displacementvectors to at least one pixel of the selected patch of the first image,the second set of random displacement vectors having a second range ofrandomness less than the first range of randomness, wherein the secondrange of randomness is a function of the first displacement vector. 7.The method of claim 1, wherein identifying a first image and a secondimage comprises identifying two portions of an image, wherein the twoportions are separated by an axis of symmetry.
 8. A system for comparingimages, the system comprising: a memory configured to store a firstimage and a second image; a graphical display configured to display anoutput; and a processor coupled to the graphical display and to thememory, the processor configured to: obtain the first image and thesecond image from the memory; partition each of the first and secondimages to create a plurality of respective patches for each image,wherein each patch comprises a plurality of pixels; determine arespective difference between at least one patch, or portion thereof, ofeach respective image and an average patch, or portion thereof, of therespective image; identify a respective selective shift for each patch,or portion thereof, of the first image to a corresponding patch, orportion thereof, of the second image, wherein each shift is independentof each other shift, wherein the selective shift represents a minimumchange in a set of pixel statistics between a respective patch, orportion thereof, of the first image and the corresponding patch, orportion thereof, of the second image; calculate a respective score forat least a portion of the patches, the score comprising the respectivedifference and the respective selective shift; and output at least onescore to the graphical display for displaying on the graphical display.9. The system of claim 8, wherein the processor is configured todetermine a respective difference by: determining at least onerespective principal component of each image; determining at least onerespective principal component of each patch of each image; matchingeach patch of the first image to a nearest match patch of the secondimage to create a set of matched patches; selecting, based on the set ofmatched patches, a subset of patches of the first image and a subset ofpatches of the second image; determining the respective differencebetween each patch of each respective subset of patches and the averagepatch for each respective image, the difference being measured withrespect to at least one principal component of the respective image. 10.The system of claim 9, wherein the processor is configured to select asubset of patches of the first image and a subset of patches of a secondimage by selecting the subset of patches based on a difference invariance between a variance of a principle component of a first patch,and a variance along a same principle component of a second patch,wherein the difference in variance is above a first threshold, whereinthe first patch and the second patch are a pair of patches in the set ofmatched patches.
 11. The system of claim 9, wherein the processor isconfigured to match each patch by: calculating at least one respectiveprincipal component of each patch of each image; identifying a pluralityof sets of associations, wherein each set comprises a plurality ofassociations such that each patch of the first image is uniquely matchedto a single patch of the second image; calculating a respective cost ofeach set of associations, wherein the cost comprises a sum of eachrespective cost of each association; and selecting the set ofassociations having a lowest cost.
 12. The system of claim 11, whereinthe processor is configured to calculate the respective cost of each setof associations by calculating the respective cost based on an angleformed between at least one principal component of a first patch and atleast one principal component of a second patch, wherein the first patchand the second patch are a pair of patches in the set of matchedpatches.
 13. The system of claim 8, wherein the processor is configuredto obtain the first image and the second image by separating a singleimage along an axis of symmetry.
 14. The system of claim 8, wherein theprocessor is configured to identify a selective shift by: assigning afirst set of random displacement vectors to at least one pixel of aselected patch of the first image, wherein the first set of randomdisplacement vectors has a first range of randomness; computing, foreach random displacement vector in the first set of random displacementvectors, a respective difference in pixel statistics of each at leastone pixel of the selected patch of the first image to a corresponding atleast one pixel of a second patch of the second image, wherein thecorresponding at least one pixel of the second patch is determined by arandom displacement vector of the first set of random displacementvectors; selecting a first displacement vector of the first set ofrandom displacement vectors having a lowest difference in pixelstatistics; and assigning a second set of random displacement vectors toeach at least one pixel of the selected patch of the first image, thesecond set of random displacement vectors having a second range ofrandomness, wherein the second range of randomness is a function of thefirst displacement vector, wherein the second range of randomness isless than the first range of randomness.